no code implementations • 19 Mar 2024 • Isidora Teofilovic, Ali Cem, David Sanchez-Jacome, Daniel Perez-Lopez, Francesco Da Ros
Here, we train and experimentally evaluate three models incorporating varying degrees of physics intuition to predict the effect of thermal crosstalk in different locations of an integrated programmable photonic mesh.
no code implementations • 10 Aug 2023 • Ali Cem, Ognjen Jovanovic, Siqi Yan, Yunhong Ding, Darko Zibar, Francesco Da Ros
We present and experimentally evaluate using transfer learning to address experimental data scarcity when training neural network (NN) models for Mach-Zehnder interferometer mesh-based optical matrix multipliers.
no code implementations • 29 Nov 2022 • Ali Cem, Ognjen Jovanovic, Siqi Yan, Yunhong Ding, Darko Zibar, Francesco Da Ros
We demonstrate transfer learning-assisted neural network models for optical matrix multipliers with scarce measurement data.
no code implementations • 17 Oct 2022 • Ali Cem, Siqi Yan, Yunhong Ding, Darko Zibar, Francesco Da Ros
Photonic integrated circuits are facilitating the development of optical neural networks, which have the potential to be both faster and more energy efficient than their electronic counterparts since optical signals are especially well-suited for implementing matrix multiplications.
no code implementations • 23 Nov 2021 • Ali Cem, Siqi Yan, Uiara Celine de Moura, Yunhong Ding, Darko Zibar, Francesco Da Ros
We experimentally compare simple physics-based vs. data-driven neural-network-based models for offline training of programmable photonic chips using Mach-Zehnder interferometer meshes.